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Increasing Precision Without Altering Treatment Effects: Repeated Measures Designs in Survey Experiments
Scott Clifford
Geoffrey Sheagley 
Spencer Piston 

The analyses were primarily completed with: 
Stata: Stata 14
R: R version 4.0.2 (2020-06-22) -- "Taking Off Again"


Stata Packages: 
[1] metareg

R Packages: 
[1] tidyverse (1.3.0)
[2] tidymodels (0.1.2)
[3] ggpubr (0.4.0)
[4] PowerUpR (1.0.4)


Replication Files (Analyses Reported in paper) 

[1] Primary Treatment Effects (Results displayed in Figure 1) 
	[1] Data: Study1data.tab, Study2data.tab, Study3data.tab, Study4data.tab, Study5data.tab, Study6data.tab 
	[2] Stata .do Files: Study1code.do, Study2code.do, Study3code.do, Study4code.do, Study5code.do, Study6code.do 


	*Additional Information: All analyses were run in Stata. 


[2] Internal Meta-Analysis (Results displayed in Figure 2) 
	[1] Data: metadata.tab 
	[2] Code: metacode.do 


[3] Perceptions of Attitude Change (Results displayed in Figure 3]
	[1] Data: Study6data.tab 
	[2] Code: Study6code.do

	*Additional Information: Code for analyses are contained at the end of the Study6.code.do file. 

[4] Minimum Detectable Effect (MDE) Analyses (Results displayed in Figure 5)
	[1] Code: MDE simulations.R 

[5] Treatment Effect Heterogeneity (Results displayed in Figure 6) 
	[1] Data: hetdata.dta 
	[2] Code: hetcode.do 


Replication Files (Analyses Reported in Online Appendix) 

[1] Alternative Meta-Analysis (Results displayed in Figure A1) 
	[1] Data: metadata_alt.tab 
	[2] Code: metacode_alt.do 


[2] Power Simulations (Results displayed in Figure A2)
	[1] Code: power simulations.R 
